Abstract
Background
Maternal nutrition can have a profound effect on fetal growth, development, and subsequent infant birth weight. However, little is known regarding the influence of prepregnancy dietary patterns.
Objectives
This study aimed to explore the effects between prepregnancy dietary patterns on birth weight.
Methods
This study included 911 singleton live-born infants from the Taicang and Wuqiang Mother–Child Cohort Study (TAWS). Baseline information and prepregnancy diet data were collected during early pregnancy. Newborn birth information was obtained from the Wuqiang County Hospital. Macrosomia, defined as a birth weight of ≥4000 g, and large for gestational age (LGA), defined as a birth weight higher than the 90th percentile for the same sex and gestational age, were the outcomes of interest. The dietary patterns were extracted using principal component analysis. Logistic regression models were used to investigate the association between prepregnancy dietary patterns (in tertiles) and macrosomia and LGA, and subgroup analysis was further explored by pre-pregnancy body mass index (BMI).
Results
Four dietary patterns were identified based on 15 food groups. These patterns were named as “cereals–vegetables–fruits,” “vegetables–poultry–aquatic products,” “milk–meat–eggs,” and “nuts–aquatic products–snacks.” After adjusting for sociodemographic characteristics, pregnancy complications, and other dietary patterns, greater adherence to the “cereals–vegetables–fruits” pattern before pregnancy was associated with a higher risk of macrosomia (adjusted OR = 2.220, 95% CI: 1.018, 4.843), while greater adherence to the “nuts–aquatic products–snacks” pattern was associated with a lower risk of macrosomia (adjusted OR = 0.357, 95% CI: 0.175, 0.725) compared to the lowest tertile. No significant association was observed between prepregnancy dietary patterns and LGA. However, after subgroup analysis of pre-pregnancy BMI, “cereals–vegetables–fruits” pattern was associated with an increased risk of LGA in overweight and obese mothers (adjusted OR = 2.353, 95% CI: 1.010, 5.480).
Conclusions
An unbalanced pre-pregnancy diet increases the risk of macrosomia and LGA, especially in overweight or obese women before pre-pregnancy.
1. Introduction
Two terms are applied to excessive fetal growth: “large for gestational age” (LGA) and “macrosomia.” LGA was defined as birth weight equal to or greater than the 90th percentile for the same gestational age [Citation1]. The term “macrosomia” implies growth beyond an absolute birth weight, historically 4000 g or 4500 g, regardless of gestational age [Citation1–4]. Previous studies have shown that macrosomia and a large gestational age have serious effects on both mothers and newborns. For mothers, it has been confirmed that macrosomia and LGA are associated with maternal and perinatal complications such as vaginal and perineal trauma, cesarean delivery, prolonged labor, and postpartum hemorrhage [Citation5–8]. For newborns, macrosomia and LGA have an increased risk of shoulder dystocia, hypoglycemia, brachial plexus injury, skeletal injuries, clavicular fracture, perinatal asphyxia, and mortality [Citation5–7]. In addition, macrosomia newborns are more susceptible to metabolic disorders, such as overweight, obesity, and hypertension, later in life [Citation9–11]. The prevalence of macrosomia and LGA is approximately 1–20% and 4–22% worldwide, and 3–14% and 7–22% in China, respectively [Citation6,Citation12,Citation13]. In rural areas of China, the incidence of macrosomia decreased from 7.96% in 2013 to 5.47% in 2017 [Citation14]. The prevalence of macrosomia and LGA in southern China from 2012 to 2021 was 3.3% and 7.4%, respectively [Citation15]. Although the prevalence of macrosomia may be lower than that of other adverse pregnancy outcomes, it still represents an important public health problem owing to the lifelong risks associated with it.
Maternal diet can directly or indirectly affect birth outcomes through epigenetic changes in the fetus [Citation16]. Despite the importance of a healthy diet during pregnancy, evidence suggests that there is little change in maternal dietary patterns before and during pregnancy [Citation17,Citation18]. Dietary interventions starting in pregnancy can reduce gestational weight gain but have little impact on birth outcomes [Citation19]. Early pregnancy is a critical period for placental and fetal tissue development, and its size of the early placenta is significantly associated with macrosomia and LGA [Citation20]. Hillesund et al. showed that high birth weight was associated only with a prepregnancy diet and not with early pregnancy dietary behavior in a post hoc observational analysis in Norway [Citation21]. Therefore, pre-pregnancy diet quality is essential for normal fetal growth and development.
To date, few studies have evaluated the association between pre-pregnancy dietary patterns and LGA, and no studies have investigated the association between pre-pregnancy dietary patterns and macrosomia. Alves-Santos et al. observed that higher adherence to fast food and candies prepregnancy dietary patterns increased the odds of LGA birth [Citation22]. Few studies have examined the association of prepregnancy dietary patterns with birth weight, low birth weight, or small for gestational age; however, assessment of prepregnancy dietary patterns on high birth weight has not been assessed [Citation23–26]. Thus, further studies are necessary to elucidate the role of maternal prepregnancy dietary patterns in excessive fetal growth.
Hence, the aim of this study was to evaluate the association between prepregnancy dietary patterns and macrosomia and LGA.
2. Methods
2.1. Study participants and design
This study was based on data from the Taicang and Wuqiang Mother–Child Cohort Study (TAWS), an ongoing maternal and child nutrition and health cohort study in China. Briefly, pregnant women were recruited from health centers or hospitals in Taicang and Wuqiang. Mother–child pairs were followed up several times during pregnancy and postpartum to examine the association between early life nutrition and children’s health. Further details on TAWS have been published elsewhere [Citation27]. All the participants provided written informed consent. This study was approved by the Medical Ethics Committee of the Chinese Center for Disease Control and Prevention (No. 2016-014).
In the present study, we included women from Wuqiang who entered the TAWS during their early pregnancy from June 2016 to December 2021 (n = 1991). The exact inclusion criteria for this cohort have been described previously [Citation27]. Maternal height and weight were measured in early pregnancy, and a Food Frequency Questionnaire (FFQ) asking about the month before pregnancy was completed. During the delivery phase, birth outcomes were extracted from the medical records at the local hospital. Finally, women (n = 911) with complete information on FFQ, newborns, and covariates were included in this study ().
Figure 1. Flow diagram of participants (n = 911) for this analysis.
2.2. Dietary assessment
Dietary food consumption patterns before pregnancy were assessed by using a FFQ. All participants were asked to complete the FFQ in one-to-one and face-to-face communication with the help of food models and food maps and with the help of trained investigators during their first study visit (i.e. in the first trimester). The FFQ contains 59 food items. Mothers were asked to report the frequency of consumption and portion sizes of these foods in the month before pregnancy. Frequency options were “per day”, “per week”, and “per week, and the number of times was filled in by the participants. Dietary intake in grams per day was estimated using the indicated frequencies of consumption that were converted to intake per day and multiplied by the weight of food consumed per session.
Food items reported in the FFQ consumed by less than 5% of the population were excluded from the analysis. Finally, 58 different food items from the FFQ were aggregated into 15 food groups according to similarities in food type and nutritional composition and on food groupings previously published in studies of dietary patterns in Chinese women [Citation28–30]. After computing the daily intake of each food group, those with unlikely daily food intake of grains only or no grains per day were excluded from analysis (n = 12).
2.3. Assessments of outcomes
Infant sex, birth weight (g), and length (cm) were extracted from the medical records of the Wuqiang County Hospital. Gestational age was determined based on the date of the last menstrual cycle. Macrosomia was defined as a birth weight ≥4000 g or more. The growth standard for newborns by gestational age based on the health industry standards of the People’s Republic of China, LGA, was defined as birth weight higher than the 90th percentile for the same gender and gestational age [Citation31].
2.4. Covariates
Several covariates were controlled in this study, including maternal age, education, physical activity, prepregnancy BMI, parity, gestational diabetes mellitus (GDM), and hypertensive disorder in pregnancy (HDP). Trained investigators used a structured questionnaire during the first visit to obtain the following baseline variables: maternal date of birth, education, occupation, parity, and maternal height and weight. Complications of Pregnancy and date of delivery were obtained at follow-up after delivery.
Maternal age at birth (years) was treated as a continuous variable and derived using the maternal date of birth and date of delivery. Maternal physical activity was assessed using maternal occupation and categorized as light, moderate, or heavy [Citation32].
The actual measured weight in the first trimester was used as a surrogate for recalled prepregnancy weight [Citation33], and participants’ pre-pregnancy body mass index (BMI) was calculated by the researcher. According to the BMI classification standard in China [Citation34], the women were classified as underweight (BMI <18.5 kg/m2), normal weight (BMI = 18.5–23.9 kg/m2), overweight (BMI = 24.0–27.9 kg/m2), and obese (BMI ≥28.0 kg/m2).
2.5. Statistical analyses
All statistical analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL). Principal component analysis (PCA) was conducted on the dietary data derived from the FFQ. First, the adaptability test of factor analysis was performed by calculating the Kaiser–Meyer–Olkin (KMO) statistic and Bartlett’s test of sphericity. Subsequently, varimax rotation of the factors was employed, and the number of components to be retained was considered at the point at which the scree plot leveled off (i.e. an eigenvalue greater than one). The coefficients defining the PCA components are called factor loadings and represent the correlations between each food variable and the PCA components. We considered food items with factor loadings with absolute values greater than 0.4 meaningful to interpret each dietary pattern. Scores were calculated to represent women’s adherence to each component retained. Scores were calculated by multiplying the standardized daily intake of each food group by its corresponding coefficient for the component, and then summing. Dietary pattern scores were further categorized as tertile 1 (low adherence), tertile 2 (moderate adherence), and tertile 3 (high adherence).
Descriptive statistics were used to characterize the population using frequency, median, and interquartile range (IQR). Prior to analysis, the normal distribution of all variables was checked using the Kolmogorov–Smirnov test. Continuous variables underlying skewed distribution were compared using the Kruskal–Wallis test for comparisons between three or more groups. Categorical variables were compared using the Chi-square test or Fisher’s exact test. The binary logistic regression model was used to examine the relationship between prepregnancy dietary patterns and macrosomia and LGA, and subgroup analysis was further performed on pre-pregnancy BMI. All statistical tests were two-sided, and statistical significance was set at p < .05.
3. Results
3.1. Participants characteristics
A total of 911 pregnant women who met the study requirements were included. Maternal age ranged from 18 to 43.5. In this study, 50.2% of the fetuses were male and 49.8% were female. A total of 92 macrosomia cases (10.1%) were reported, and the prevalence of macrosomia was higher in overweight/obese women and women with GDM. On the other hand, 173 LGA (19.0%) were reported among 911 live birth singletons. The prevalence of LGA was higher in women with an educational status of primary school or below, overweight/obese women, and women with GDM ().
Table 1. Maternal characteristics according to preterm birth.
3.2. Dietary pattern analysis
The results showed that KMO = 0.835 and Bartlett’s spherical test was p < .001. These values were acceptable, indicating that the data were suitable for factor analysis. Among the 15 food groups, four factors were retained and examined as dietary patterns (). These patterns cumulatively explained 51.5% of the total variance in diet. The first pattern was labeled “cereals–vegetables–fruits” and had high positive factor loadings for cereals, tubers, dark vegetables, light vegetables, and fruits. The second pattern, “vegetables–poultry–aquatic products” had high positive factor loadings for dark vegetables, light vegetables, mushrooms and algae, red meat, poultry, meat products, fish, shrimp, and other aquatic products. The third, “milk–meat–eggs” had positive factor loadings for milk, meat, meat products, and eggs. The fourth, “nuts–aquatic products–snacks” had high positive factor loadings for nuts, bread, biscuits, chocolate and other snacks, fish, shrimp and other aquatic products. The percentage of variance explained by these four factors was 16.5%, 13.8%, 11.0%, and 10.2%.
Table 2. Factor loading matrix for the four dietary patterns.
3.3. Association of prepregnancy dietary patterns with macrosomia and LGA
Binary logistic regression, which was used to analyze the association between scores on pre-pregnancy dietary patterns of mothers and macrosomia and LGA (), found higher scores on the “cereals–vegetables–fruits” dietary pattern were positively associated with macrosomia (Q2 vs. Q1, OR = 1.981, 95% CI: 0.976, 4.022; Q3 vs. Q1, OR = 2.220, 95% CI: 1.018, 4.843) in the fully adjusted model. Compared with tertile 1, women in tertile 3 of the “nuts–aquatic products–snacks” dietary pattern had a lower risk of macrosomia with or without adjusting for covariates (fully adjusted OR = 0.357, 95% CI: 0.175, 0.725). The “nuts–aquatic products–snacks” dietary pattern was associated with a reduced risk of LGA after adjustment for the other dietary patterns (adjusted OR = 0.617, 95% CI: 0.376, 1.011), p = .055. However, this association was no longer significant in the fully adjusted model.
Table 3. Logistic regression results of the associations between the dietary pattern scores and macrosomia and LGA.
Pre-pregnancy BMI was further grouped to explore the association between pre-pregnancy dietary patterns and LGA (). The pre-pregnancy underweight and normal weight groups were merged as the healthy weight group because of the small number of women categorized as underweight (6.2%), and those with overweight and obesity were grouped together. The results showed that in the overweight and obese groups, those who had higher scores on the “cereals–vegetables–fruits” dietary pattern had a greater risk of LGA (Q3 vs. Q1, OR = 2.353; 95% CI: 1.010–5.480) in the fully adjusted model.
Table 4. Logistic regression results of the associations between the dietary pattern scores and LGA in different pre-pregnancy BMI groups.
4. Discussion
In this study, dietary patterns before pregnancy were examined and associations with macrosomia and LGA were assessed. Four interpretable dietary patterns were derived, which explained 51.5% of the variance in the diet. These patterns were named “cereals–vegetables–fruits”, “vegetables–poultry–aquatic products”, “milk–meat–eggs”, and “nuts–snacks–aquatic products”, reflecting the food groups that characterized them. We found that the highest adherence to the “cereals–vegetables–fruits” pattern before pregnancy compared to the lowest adherence was associated with a high risk (OR = 2.220, 95% CI: 1.018, 843) of macrosomia, and the highest adherence to this pattern also increased the risk (OR = 2.353, 95% CI: 1.010, 5.480) of LGA among mothers who were overweight and obese before pregnancy. Moreover, women with the highest adherence to the “nuts–snacks–aquatic products” pattern before pregnancy presented a 64% lower risk (OR = 0.357, 95% CI: 0.175, 0.725) of macrosomia.
In this study, women with high adherence to the “cereals–vegetables–fruits” pattern before pregnancy were more likely to have macrosomia than those with low adherence. Increased maternal fruit intake before and during pregnancy is associated with increased birth weight [Citation23,Citation35]. A prospective study conducted in Singapore proved that a vegetable, fruit, and white rice dietary pattern during pregnancy was associated with higher birth weight, higher ponderal index, and increased risk of LGA deliveries [Citation36]. Another prospective study among Japanese women found that women in the "rice, fish, and vegetables" group during pregnancy might be associated with a large birth weight and a decreased risk of having a SGA infant [Citation37]. Despite the variety of foods consumed and the differences in constructed dietary patterns across countries and cultures, this study and the previous studies mentioned above together suggest that higher birth weight is associated with dietary patterns containing rice, vegetables, and fruits. Women with greater adherence to the “cereals–vegetables–fruits” pattern had higher intakes of cereals, tubers, and fruits, which are generally regarded as high-carbohydrate and high-glycemic index foods. They may influence maternal blood glucose levels and intrauterine nutritional environment, and possibly increase intrauterine growth rate and fetal fat accretion [Citation38,Citation39]. Whether the higher intakes of those foods on the “cereals–vegetables–fruits” pattern contribute to the higher risk of macrosomia and LGA warrants further investigation.
“Cereals–vegetables–fruits” pattern could increase the risk of LGA among pre-pregnancy overweight and obese mothers. However, no association was observed between this pattern and LGA in women with a healthy pre-pregnancy BMI. It is well established that a higher maternal BMI is associated with a greater risk of delivering a newborn with high birth weight [Citation40]. Maternal pre-pregnancy overweight and obesity are risk factors for LGA [Citation41–43]. The burden of macrosomic and LGA neonates attributable to high pre-pregnancy BMI increased among Chinese females with planned pregnancies during 2013–2017 [Citation44]. Changes in maternal levels of nutrients, growth factors, and hormones in maternal obesity modulate placental function, thereby increasing fetal nutrient supply and contributing to fetal overgrowth and/or adiposity in the offspring [Citation45]. Studies in animal models have shown that maternal overnutrition or intake of high-fat and high-energy diets in the preconception period and during pregnancy reprograms hypothalamic appetitive systems and orexigenic pathway in the offspring, which may contribute to overweight or obesity later in life [Citation46]. Thus, to improve adherence to healthy dietary practices and reduce the risk of LGA, healthcare professionals may need to discuss the importance of maintaining prepregnancy body weight and following the Chinese Nutrition Society food intake guidelines for women. In addition, pre-pregnancy BMI may be a causal pathway of the association between dietary intake and LGA. Therefore, additional research is needed to quantify the mediating effects of prepregnancy BMI on the association between prepregnancy dietary patterns and LGA.
Although our “nuts–aquatic products–snacks” pattern contains bread, biscuits, cakes, and other snacks, which are believed to be risk factors for obesity development, the pattern also contains high-protein foods such as fish, shrimp, and eggs. The snacks in this study also contained whole wheat bread, crackers, and other whole wheat foods. Oats, barley, and other coarse cereals, which are rich sources of fiber, provide high satiety values, decrease appetite, and help in weight management. Although nuts are energy-dense, some nutrients, such as protein, unsaturated fat, and fiber, cause satiation, limiting the consumption of additional calories. It was observed that the ingestion of nuts can help control satiety and increase thermogenesis. They are rich in monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), proteins, fibers, vitamins, minerals, and bioactive compounds with antioxidant potential that may play a protective role against metabolic disorders [Citation47,Citation48]. In addition, Shao et al. argued that the dietary intake of certain fish and shrimp species may benefit human health by alleviating inflammatory responses [Citation49]. This may account for the low risk of macrosomia in mothers with high adherence to the “nuts–aquatic products–snacks” dietary pattern.
The present study had some limitations. First, the FFQ was administered in the first trimester, and mothers were asked to recall their diet in the month before pregnancy, which may have introduced bias owing to possible changes in diet during early pregnancy. Second, we followed only women who gave birth at Wuqiang County Hospital because of loss to follow-up, which may be subject to selection bias. Third, although the study was based on a community population, which may provide relatively good generalizability to a local population, our findings are based on a rural population with a relatively low socioeconomic status; thus, they may not generalize to other settings. Lastly, residual confounding linked with both fetal growth and maternal preconception diet cannot be excluded, although we adjusted for several factors in the statistical analysis.
Nevertheless, the strengths of our study include its prospective design, which is important for establishing the temporal sequence between maternal preconception dietary patterns and birth outcomes. Our study also fills the knowledge gap regarding maternal preconception dietary patterns in a relatively understudied Asian population. In addition, in this study, the standard for evaluating newborn weight by gestational age and sex was based on newborns from 69 hospitals in 13 cities in China, from 2015 to 2018, which is suitable for the growth level of newborns in China [Citation31].
5. Conclusions
In conclusion, we found that prepregnancy dietary patterns are associated with newborn birth weight. A dietary pattern containing cereals, tubers, vegetables, and fruits is associated with an increased likelihood of macrosomia and an increased risk of LGA in mothers who are overweight or obese before pregnancy, whereas a dietary pattern mainly consisting of nuts, fish, shrimp, other seafood, eggs, bread biscuits, and other snacks is associated with a reduced likelihood of macrosomia. Our findings highlight the importance of promoting a healthy diet before pregnancy. It is recommended that mothers have a balanced and varied diet, increase the proportion of nuts and seafood in their diet, and control maternal pre-pregnancy BMI to improve birth outcomes and long-term health of the child.
Author contributions
The authors’ responsibilities were as follows: YiZ, YoZ, YD, CL, and ZC designed the study; YiZ and JD analyzed the data and interpreted the results; YiZ and YoZ wrote the manuscript; YD, CL, ZY, and ZC provided critical input to the manuscript; and all authors read and approved the final manuscript.
Acknowledgements
This study was conducted as part of the Taicang and Wuqiang Mother–Child Cohort Study (TAWS) by the National Institute for Nutrition and Health. We are grateful to all collaborating organizations and staff of TAWS and all study participants.
Disclosure statement
The authors report no conflicts of interest.
Data availability statement
The data supporting the findings of this study are available from the corresponding author, ZC, upon reasonable request.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
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